Atlas and methods for segmentation and alignment of anatomical data

ABSTRACT

The present invention provides an atlas comprising values representative of magnetic resonance properties of a magnetic resonance (MR) scan and optionally, prior probability data relating to tissue type. Further embodiments of the invention involve a system including an MR scanner and the atlas for use in alignment of an MR scan, such as a localizer scan, to obtain a specific geometry of the data acquired during a subsequent scan. Also, a system includes an MR scanner and the atlas for automatic segmentation of an MR scan. Methods of making and using the atlas and system are also provided.

TECHNICAL FIELD

[0001] The present invention generally relates to magnetic resonance andother biological scan data.

BACKGROUND

[0002] Magnetic resonance imaging is a complex interaction betweenprotons in biological tissues, a static and alternating magnetic field(the magnet), and energy in the form of radio-frequency waves of aspecific frequency (RF), introduced by coils placed next to the subject.The energy state of the hydrogen protons is transiently increased. Thesubsequent return to equilibrium (relaxation) of the protons results inthe release of RF energy which can be measured by the same surface coilsthat delivered the RF pulses. The RF energy, also referred to as the RFsignal or echo, is complex and is thus transformed by Fourier analysisinto useful information used to form an MR image.

SUMMARY

[0003] The present invention provides apparatus and methods forprocessing data associated with magnetic resonance (MR) scanning. Inparticular, in one embodiment, the present invention provides an atlascomprising at least one value representative of a magnetic property and,optionally, at least one value representative of tissue type priorprobability. In a further embodiment, the present invention provides anatlas comprising a plurality of values representative of magneticproperties of a plurality of spatial locations of a plurality ofsubjects. In one embodiment, a system is provided having both an MRscanner and an atlas of the present invention. In a further embodiment,the invention provides methods of making and using the atlas and system.

[0004] The apparatus and methods of the present invention provide amodel having data representative of one or more subjects. The dataincludes magnetic property values, optionally, tissue type priorprobability values. The atlas can be used to automatically align an MRscan, such as a localizer scan, to obtain a specific geometry of thedata acquired during a subsequent scan. The atlas may also be used toautomatically identify, or segment, tissue type of a subject based on MRscan data of the subject.

[0005] According to one embodiment of the invention, an atlas isprovided comprising a plurality of values representative of a magneticproperty of a plurality of spatial locations of a subject as determinedby magnetic resonance. According to a further embodiment, an atlas isprovided comprising values representative of a statisticalrepresentation of a magnetic property of a plurality of spatiallocations of a plurality of subjects. The present invention alsoprovides a system comprising an MR scanner and an atlas. For example,the atlas may contain magnetic property data. The system can be used toautomatically align an MR scan, such as a localizer scan, to obtain aspecific orientation of the data acquired during a subsequent scan. Thesystem may also be used to automatically identify, or segment, tissuetype of a subject based on MR scan data of the subject.

[0006] Methods of using the atlas are further provided herein. In oneembodiment, a method of using the atlas having magnetic property valuesto obtain a specific geometry of data to be acquired during a subsequentscan is provided. In a variation of this embodiment, a method of usingthe atlas may additionally involve tissue type probabilities.

[0007] Methods of using the atlas are further provided herein. In oneembodiment, a method of using the atlas having magnetic property valuesto determine tissue type is provided. In a variation of this embodiment,a method of using the atlas may additionally involve tissue typeprobabilities.

[0008] According to a further embodiment of the invention, a method isprovided for obtaining information about a subject having the steps ofproviding a magnetic resonance scanner, providing an atlas havingmagnetic resonance data derived from at least one other subject andprocessing information received from the scanner pertaining to thesubject. Also included are the steps of reading the atlas anddetermining alignment of the magnetic resonance scan to obtain aspecific geometry of a subsequent magnetic resonance scan.

[0009] According to another embodiment of the invention, another methodis provided for obtaining information about a subject. This methodinvolves the steps of providing magnetic property values correspondingto tissue types pertaining to the subject, providing an atlas havingmagnetic property values derived from at least one other subject, alongwith labeling tissue types of a tissue corresponding to the magneticresonance property values pertaining to the subject by using the atlashaving the magnetic resonance values derived from at least one othersubject.

[0010] According to a further embodiment of the invention, a method isprovided for creating an atlas by providing a first magnetic resonancemodality volume pertaining to a subject, divided into voxels, andrecording a magnetic property value in a node of the atlas correspondingto a voxel of the first magnetic resonance modality volume.

[0011] Another embodiment of the invention involves a method forcreating an atlas. A first magnetic resonance modality volume isprovided pertaining to a subject and divided into voxels. A labeledvolume is provided indicating tissue types of tissue corresponding tothe voxels. Distortion of the first magnetic resonance modality volumeis corrected. Magnetic property distribution parameters are extractedfor each tissue type identified at each voxel. Also, magnetic propertydata is recorded corresponding to each tissue type in a node of theatlas corresponding to a voxel of the first magnetic resonance modalityvolume.

[0012] According to another embodiment, a method for creating an atlasis provided wherein a voxel intensity is obtained from an imagerepresentative of at least one magnetic modality of a voxel of asubject, a magnetic property value is derived from the voxel intensity,and the magnetic property value is written to a node of the atlascorresponding to the voxel.

[0013] A further embodiment of the invention provides a method forprocessing an image of a subject. An atlas is provided having magneticproperty values derived from at least one other subject. The image isaligned to the atlas, and the image is segmented into segments. Thesegments are labeled to designate a tissue type of a tissuecorresponding to the magnetic property values pertaining to the subjectby the use of the atlas. An image is thus obtained pertaining tomagnetic property values of the subject.

[0014] It will further be appreciated that in the methods of the presentinvention, distortion may be corrected prior to entering the data intothe atlas, as well as prior to processing newly acquired data inconjunction with the atlas.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The invention will be apparent from the description herein andthe accompanying drawings, in which like reference characters refer tothe same parts throughout the different views.

[0016]FIG. 1 provides a subject and a grid pattern illustrating voxelsof a subject;

[0017]FIG. 2 illustrates an atlas;

[0018] FIGS. 3-8 illustrate nodes of an atlas according to variousembodiments of the invention.;

[0019]FIGS. 9A and 9B illustrate sample data for determination of thecontent of a node of an atlas;

[0020]FIG. 10 provides a sample method for the creation of an atlas;

[0021]FIG. 11 provides a sample method for the registration of MR datato an atlas; and

[0022]FIG. 12 provides a functional schematic of a system according toan embodiment of the invention.

DETAILED DESCRIPTION

[0023] The present invention, in various embodiments, involves an atlascontaining values representative of magnetic properties of a magneticresonance (MR) scan and optionally prior probability data relating totissue type. Further embodiments of the invention involve a systemincluding an MR scanner and the atlas for use, for example, in alignmentof an MR scan and for automatic segmentation of an MR scan. Methods ofcreating and using the atlas and system are also provided.

[0024] As used herein, the following terms are defined as follows:

[0025] T1 and T2 relaxation times: The rate of return to equilibrium ofperturbed protons is referred to as the relaxation rate. The relaxationrate is different for different normal and pathologic tissues. Therelaxation rate of a hydrogen proton in a tissue is influenced bysurrounding molecular environment and atomic neighbors. Two relaxationrates, the T1 and T2 relaxation times, may be measured. The T1relaxation rate is the time for 63% of the protons to return to theirnormal equilibrium state, while the T2 relaxation rate is the time for63% of the protons to become dephased owing to interactions amongadjacent protons. The intensity of the signal and thus the imagecontrast can be modulated by altering certain parameters, such as theinterval between RF pulses (TR) and the time between the RF pulse andthe signal reception (TE). So-called T1-weighted (T1W) images areproduced by keeping the TR and TE relatively short. Under theseconditions, contrast between structures is based primarily on their T1relaxation differences. T2-weighted (T2W) images are produced by usinglonger TR and TE times.

[0026] TR: The time between repetitions of RF in an imaging sequence.

[0027] TE: The time between the RF pulse and the maximum in the echo ina spin-echo sequence.

[0028] Flip Angle: The angle that the magnetic moment vector rotateswhen applying a B1 RF pulse field.

[0029] T1: The time to reduce the difference between the longitudinalmagnetization and its equilibrium magnetization by an exponentialfactor.

[0030] T2: The time to reduce the transverse magnetization by anexponential factor.

[0031] PD: The concentration of spins.

[0032] T1-weighted: A magnetic resonance image where the contrast ispredominantly dependent on T1.

[0033] T2-weighted: A magnetic resonance image where the contrast ispredominantly dependent on T2.

[0034] PD-weighted: A magnetic resonance image where the contrast ispredominantly dependent on PD.

[0035] Diffusion-weighted: A magnetic resonance image where the contrastis predominantly dependent on diffusion weighting gradient.

[0036] Magnetization Transfer-weighted: A magnetic resonance image wherethe contrast is predominantly dependent on magnetization transfersaturation effect.

[0037] Tissue Type: As used herein, “tissue type” can be used todesignate a classification or characteristic of a tissue, such as tissuewithin a voxel. For example, when used with a human brain as thesubject, tissue type can include, without limitation, gray matter, whitematter and cerebral spinal fluid. Optionally, the tissue type can bemore specific, such as referring to anatomical structure. For example,in the case of a brain as the subject, the tissue type may designategray matter and/or, more specifically, hippocampus, or other appropriateanatomical structure label. In another example, in the case of a spineas a subject, the tissue type may designate bone, and/or morespecifically vertebral bodies, or other appropriate anatomical structurelabels. In yet another example, in the case of the kidney as a subject,the tissue type may designate the cortex, and/or more specificallynephrons, or other appropriate anatomical structure labels.

[0038] Localizer scan: A low-resolution scan acquired at the beginningof a scanning procedure to estimate the precision of the acquisitiongeometry relative to the subject to be imaged.

[0039] Subsequent scan: A high-resolution scan acquired on the basis ofthe localizer geometry, such as orientation, dimensions, or voxel size.

[0040] Magnetic property: A magnetic property of protons, such as T2,T1, PD, diffusion or magnetization transfer.

[0041] The present invention is applicable to a wide variety of MR scansof a subject including mammals (e.g. humans), as well as specificportions of a subject (e.g., organ, limb, or a portion of an organ orlimb), also referred to herein as the “subject”. Each subject is dividedin three-dimensional space into voxels. With reference to FIG. 1, asubject 100, such as a human brain, is shown with an illustrative gridpattern 200 signifying the locations of voxels 210. Each voxel 210represents a three-dimensional portion of the subject 100. A voxel 210may be of various dimensions and can have different dimensions alongdifferent axes within the subject 100.

[0042] As shown in FIG. 2, an atlas 300 is provided according to anembodiment of the invention. While illustrated as a three-dimensionalstructure, the invention is not so limited, as the atlas 300 may beformed of any of a variety of data structures as will be apparent to oneof ordinary skill in the art. The atlas 300 includes nodes 310.According to an embodiment of the invention, each node 310 correspondsto a voxel 210 (cf. FIG. 1) representing a portion of the subject 100.Alternatives of the invention may involve fewer nodes 310 than voxels210. In such a case, a node 310 may be configured to represent aplurality of voxels 210 or the nodes 310 may represent only a subset ofthe overall voxels 210.

[0043] FIGS. 3-8 provide various configurations of the nodes 310according to alternative embodiments of the invention. Each node 310 isconfigured to store information relating to the corresponding voxel 210.As shown in FIG. 3, the node 310 may be configured to have a magneticproperty 320 corresponding to the voxel 210. magnetic properties mayinclude, but are not limited to, T1, T2, proton density (PD), T2*,magnetization transfer, diffusion tensor and derived variables, such asanisotropy and diffusivity. According to one embodiment of theinvention, the magnetic properties may be computed from the images,based on a forward model, and the MR acquisition parameters, including,but not limited to, TR, TE, and flip angle. Determination of suchmagnetic properties and details regarding the MR acquisition parameterscan be found in Magnetic Resonance Imaging, Physical Principle andSequence Design, E. M. Haacke et al., Wiley-Liss, 1999, pp. 637-667,which is incorporated herein by reference.

[0044] Optionally, a second magnetic property 330 corresponding to thevoxel 210 may also be stored in the node 310. Additional magneticproperties may also be stored in the node 310.

[0045] A tissue type prior probability 340 corresponding to a tissuetype found in the voxel 210 may optionally be stored in the node 310.When used with a human brain as the subject, tissue type can include,without limitation, gray matter, white matter and cerebral spinal fluid.Optionally, the tissue type can be more specific, such as referring toanatomical structure. For example, in the case of a brain as thesubject, the tissue type may designate gray matter and/or, morespecifically, the hippocampus, or other appropriate anatomical structurelabel. In another example, in the case of the spine as a subject, thetissue type may designate bone, and/or more specifically vertebralbodies, or other appropriate anatomical structure labels. In yet anotherexample, in the case of the kidney as a subject, the tissue type maydesignate the cortex, and/or more specifically nephrons, or otherappropriate anatomical structure labels. It will be appreciated that thetissue type of the voxel 210 may be determined by human labeling or maybe determined by other known methods such as an algorithm (e.g. AdaptiveSegmentation of MRI Data, Wells W M, at al., IEEE Transactions onMedical Imaging, 1996;15:429—442 (corrected version available athttp://citeseer.nj.nec.com/cache/papers/cs/19782/http:zSzzSzsplweb.bwh.harvard.edu:8000zSzpageszSzpplzSzswzSzpaperszSztmi-96.pdf/wells96adaptive.pdf),Statistical Approach to Segmentation of Single-Channel Cerebral MRImages, Rajapakse J C, et al., IEEE Transactions on Medical Imaging,1997, Vol. 16, No.2: 176-86, and Automated Model-Based Bias FieldCorrection of MR Images of the Brain, Van Leemput, K. et al., IEEETransactions on Medical Imaging, 1999, Vol. 18, No. 10), which areincorporated herein by reference.

[0046] According to a further embodiment of the invention, a node 310may include a tissue type prior probability 340 corresponding to atissue type found in the voxel 210, as illustrated in FIG. 4. Accordingto this embodiment, a first magnetic property 320 is also stored.Optionally, a second magnetic property 330, or additional magneticproperties, may also be stored in the node 310.

[0047] According to a further embodiment of the invention, as shown inFIG. 5, one or multiple magnetic properties may be determined for eachof the tissue types located at the corresponding voxel 210. Therefore,as shown by way of example in FIG. 5, if a voxel 210 has two tissuetypes located at the voxel 210, as determined from a plurality ofsubjects, one or more magnetic properties 320, 330 may be stored foreach of the tissue types. As shown in FIG. 5, a value of a firstmagnetic property 320 may be stored for the tissue type 1 at thecorresponding voxel. Optionally, a value of a second magnetic property330 may also be stored for tissue type 1. Separate magnetic properties320, 330 may also be stored for the values corresponding to the tissueof tissue type 2. This variation of the invention is useful inconjunction with an atlas 300 formed of information from more than onesubject 100. A tissue type probability 340 may also be optionally storedin the node 310 for one or more of the tissue types detected at thecorresponding voxel 210.

[0048] In a further embodiment, illustrated by way of example in FIG. 6,a tissue type prior probability 340 may be stored at a node 310 for eachtissue type located at a corresponding voxel 210. A magnetic property320 is also stored at the node 310 for each tissue type. Optionally, oneor more further magnetic properties 330 may also be stored at the node310.

[0049] A further embodiment of a node 310 is illustrated in FIG. 7. Thenode 310 of FIG. 7 provides a tissue type prior probability 340 andstatistical data pertaining to a magnetic property of the tissue of acorresponding voxel 210, relative to a plurality of subjects. As shownby way of example in FIG. 7, a mean 322 of the values of a firstmagnetic property for a first tissue type at the corresponding voxel 210is provided. A variance 324 of the values of a first magnetic propertyfor the first tissue type at the corresponding voxel 210 is alsoprovided.

[0050] The node 310 of FIG. 7 may also optionally include statisticaldata pertaining to one or more additional magnetic properties, such as amean 332 and variance 334 of a second magnetic property.

[0051] The node 310 of FIG. 7 is also optionally suitable for use withan atlas 300 containing information from a plurality of subjects 100.Any of the data 322, 324, 332, 334, 340 as described above in relationto a first tissue type, may also be determined in relation to a secondtissue type and stored.

[0052]FIG. 8 illustrates a node 310 of a further embodiment of theinvention providing statistical data, such as a mean 322 and a variance324, of the values of a first magnetic property for a first tissue typeat a corresponding voxel 210. Optionally, further statistical data 332,334 or a tissue type prior probability 340 may be provided. Similarinformation 322, 324, 332, 334, 340 may also be optionally providedrelating to further tissue types at a corresponding voxel 210.

[0053] As illustrated by way of example in FIGS. 9A and 9B, thedetermination of a mean 322 and a variance 324 for a first magneticproperty can be determined. FIG. 9A provides a table 400 having thesample magnetic property values for an analogous voxel of each of threesubjects. FIG. 9B illustrates the three steps 410, 420, 430 involved indetermining the content of the node 310 corresponding to theillustrative voxel. As shown in step 1, 410, the tissue type is 1, themean of the value is 100 and the variance is 0. The prior probability ofthis node corresponding to a voxel of tissue type 1 is 1. Step 2, 420,adds the data of the second subject to the data already tabulated fromthe first subject. Therefore, the mean now rises to 150, while theremaining data is unchanged, as the tissue type is 1 for both subjects,leaving the prior probability at 1.

[0054] Step 3, 430, illustrates a node configuration illustrated in FIG.7 or 8 by the tabulation of statistical data per tissue type for eachnode. Because the tissue type for the third subject is 2, a second setof statistical data is tabulated for the new tissue type, while thefirst set of data is updated in view of the third subject. The mean andvariance of tissue type 1 remain unchanged. The prior probability oftissue type 1, however, now changes to ⅔. The mean of tissue type 2 is50, and the variance 0. The prior probability of tissue type 2 is ⅓.

[0055] In another embodiment, additional data may be stored at each noderelating to the corresponding voxel or a representation thereof. Forexample, image intensity data, expressed in arbitrary units, may bestored. Alternatives include those apparent to one of skill in the art.

[0056] In another embodiment, global prior probabilities may be storedin the atlas of the present invention. Global probabilities indicate theoverall prior probability of something, such as a tissue type appearingin a particular area, or anywhere, in a subject. The global mean andvariance of various magnetic properties may also be determined andstored for each tissue type. Such global values may be stored at avariety of locations in the atlas, such as in a header, oralternatively, at each node.

[0057] As shown by way of example in FIG. 10, a method 500 is providedaccording to an embodiment of the invention for the creation of an atlas300. The atlas is built from one or more subject data sets 510, 512,514. A subject data set may contain at least one MR scan 516, 518, 520of a subject (e.g. an organ or a portion of an organ). The MR scans canbe, but are not limited to, T1, T2, proton density (PD), T2*,magnetization transfer, diffusion tensor or derived variables such asanisotropy and diffusivity.

[0058] Distortions are then corrected in the MR scan 516, step 530.Corrections of distortion are known to one of ordinary skill in the artand are discussed in more detail in relation to FIG. 11 herein.

[0059] According to one embodiment, a subject's data set used increating or adding to an atlas can also contain a labeled representation522, 524, 526 of the MR scan(s), such as a segmented volume identifyingeach tissue type/anatomical structure. The labeled representation can beobtained by way of manual labeling (e.g. by experienced anatomists)and/or by way of automatic segmentation methods as described by way ofexample in Wells, supra, Statistical Approach to Segmentation ofSingle-Channel Cerebral MR Images, Rajapakse J C, et al., IEEETransactions on Medical Imaging, 1997, Vol. 16, No.2: 176-86, andAutomated Model-Based Bias Field Correction of MR Images of the Brain,Van Leemput, K. et al., IEEE Transactions on Medical Imaging, 1999, Vol.18, No. 10, which are incorporated herein by reference.

[0060] Next, the tissue type and corresponding magnetic propertystatistical distribution data is extracted from the corrected subjectdata set 510, step 540.

[0061] A high-resolution temporary atlas 560, step 550, is then createdby storing the tissue type and corresponding magnetic propertystatistical data of each voxel 210 of the subject, in each correspondingnode 310 of the atlas 300.

[0062] The high-resolution temporary atlas 560 may then be used as theatlas 300 if the atlas 300 is to only have data pertaining to a singlesubject.

[0063] However, if additional subjects are to be added, the method 500continues with the subject data set 512 of a second subject, and,optionally subject data sets 514 of additional subjects. Correction ofdistortion, step 530, and extraction of statistical data 540 isconducted as in relation to the first subject data set 510.

[0064] After each additional subject data set 512, 514 is processed, thetissue type and corresponding magnetic property statistical data of eachvoxel 210 of the subject is registered, or aligned, with the existingnode structure of the atlas 300, step 570. During registration, thedata, such as tissue type and magnetic statistical data, correspondingto the voxels 210 of the subject, is manipulated to correspond to theanalogous voxels 210 represented by the node 310 structure of the atlas.Further details of registration, step 570, are discussed in detail inrelation to FIG. 11 herein.

[0065] Next, the additional data, such as tissue type and magneticstatistical data, is then added to the atlas 300 by updating the atlasparameters, step 580. As shown in FIG. 10, a high-resolution atlas 565is produced after the addition of two subject data sets 510, 512 to theatlas 300. This high-resolution atlas 565 may be used as an atlas 300,or additional subject data sets 514 may be added.

[0066] When the desired N subject data sets have been added to theatlas, the atlas may optionally be subsampled, step 590 to create theatlas 300. As discussed herein, alternatives of the invention mayinvolve fewer nodes 310 than voxels 210. In such a case, a node 310 maybe configured to represent a plurality of voxels 210 or the nodes 310may represent only a subset of the overall voxels 210. Such a reducedresolution may also be generated by the subsampling, step 590, bycombining data from multiple voxels into one node. Also, only a portionof the voxels representing a portion of the subject may be used in theatlas 300.

[0067] An atlas 300 of the present invention may be customized for aspecialized purpose. The atlas may have values of a statisticalrepresentation that are population-specific (e.g., related to age, sexand/or pathology), scanner-specific (e.g., related to manufacturerand/or scanner model), and/or acquisition sequence-specific (e.g.,related to flash and/or inversion recovery). Acquisition sequences caninclude including, without limitation, at least one from the group ofPD-, T2-, T1-, diffusion-, and magnetization transfer-weighted.Acquisition sequence-specific values may involve magnetic resonancesequence parameters, including, without limitation, at least one fromthe group of TR, TE and flip angle.

[0068] An atlas of the present invention may be oriented to variouscoordinate systems. One such example of a coordinate system is aCartesian coordinate system, such as a Right Anterior Superior (RAS)coordinate system, used in orienting an image relative to a subject, oran arbitrarily determined coordinate system.

[0069] An atlas of the present invention may be created at variousspatial resolutions. An atlas may further be sub-sampled to reduce theresolution and data required and time required for calculations. Theresolution may also vary within an atlas, allowing greater resolution atareas of interest.

[0070] According to one embodiment of the invention, an atlas may beconstructed as shown in FIGS. 10 and 11. Optionally, an atlas may beformed by data from only one subject. An atlas may be formed by Nsubjects, which may be determined by monitoring the change of valueswithin the atlas upon the addition of each additional subject. Accordingto another embodiment, when the values stored in the nodes of the atlasno longer vary within a statistical range of confidence, the addition offurther subjects is no longer required.

[0071] The registration of data onto the atlas may comprise thedetermination of at least 6 parameters. For example, those parameterscan be 3 translation shifts, 3 scaling factors and 3 rotation anglesrelatively to the 3 orthogonal directions of the atlas coordinatesystem.

[0072] Further detail regarding registration of data onto an atlas, ortemporary atlas as described in FIG. 10, is illustrated by way ofexample in the method 700 of FIG. 11. In FIG. 11, a method 700 isprovided according to an embodiment of the invention for theregistration of MR data to an atlas 300. The example method 700 of FIG.11 is also applicable to prior probability data or any other data typesfor association to nodes 310 of the atlas 300.

[0073] An initial set of registration parameters is provided, step 710,along with an initial bias estimate, step 720, according to methodsknown to one of skill in the art in relation to atlases having othertypes of data. See, for example, Wells, supra, Automated Model-BasedBias Field Correction of MR Images of the Brain, Van Leemput, K. et al.,IEEE Transactions on Medical Imaging, 1999, Vol. 18, No. 10, andAutomatic Scan Prescription for Brain MRI, Itti, L. et al., MagneticResonance in Medicine, 2001, Vol. 45: 486-494, which are incorporatedherein by reference. The initial bias estimate of step 720 adjusts forintensity fall-off in the portions of the image away from the imagecenter.

[0074] A magnetic resonance (MR) volume is also provided, step 730. Themagnetic resonance volume can be generated by deriving a magneticproperty value for a voxel from a voxel intensity value of acorresponding voxel of an image containing magnetic resonance data.

[0075] A bias correction is applied to the MR volume, step 740. Withregard to step 740, and step 530 of FIG. 10, distortion and bias can becaused by a variety of factors. For example, the distortion and bias canbe subject-dependent, such as from, but not limited to, chemical shift,magnetic susceptibility, and/or per-acquisition motion. Alternatively orin addition, distortion and bias can be scanner-dependent, such as from,but not limited to, gradients non-linearity, main magnetic fieldnon-homogeneity and/or eddy currents. Maxwell effects are a furthersource of potential distortion or bias.

[0076] Correction of such distortion and bias are known to one ofordinary skill in the art.

[0077] As shown in FIGS. 10 and 11, bias and distortion are correctedprior to incorporating the data into the atlas. According to a furtherembodiment of the invention, distortion and bias are corrected prior toprocessing data in conjunction with the atlas.

[0078] A transform is applied to the bias-corrected MR volume, step 750.Linear transformations (e.g. translation, rotation, scaling) are appliedto images via homogeneous matrices. According to one embodiment, theyare 4×4 matrices, wherein the 3 first bottom elements always equal 0 andthe last bottom elements always equals 1. Any transformation can bedecomposed into a translation, a rotation and a scaling matrices. Thefinal homogeneous matrix is then a multiplication of those 3 matrices.Details are given by way of example below:

[0079] tx, ty and tz being the translation parameters in the x, y and zdirections, the translation homogeneous matrix is given by:${\begin{pmatrix}1 & 0 & 0 & t_{x} \\0 & 1 & 0 & t_{y} \\0 & 0 & 1 & t_{z} \\0 & 0 & 0 & 1\end{pmatrix}\quad}\quad$

[0080] xs, ys and zs being the scaling parameters in the x, y and zdirections, the scaling homogeneous matrix is given by: $\begin{pmatrix}x_{s} & 0 & 0 & 0 \\0 & y_{s} & 0 & 0 \\0 & 0 & z_{s} & 0 \\0 & 0 & 0 & 1\end{pmatrix}\quad$

[0081] θ, φ and Φ being the rotation parameters relatively to the x, yand z axis, the rotation homogeneous matrix is given by:$\begin{pmatrix}{{\cos \quad {\phi cos}\quad \varphi} + {\sin \quad {\phi sin}\quad {\theta sin}\quad \varphi}} & {{\sin \quad {\phi cos}\quad \theta} - {\cos \quad {\phi sin}\quad {\theta sin}\quad \varphi}} & {\cos \quad {\theta sin}\quad \phi} & 0 \\{{- \sin}\quad {\phi cos}\quad \theta} & {\cos \quad {\phi cos}\quad \theta} & {\sin \quad \theta} & 0 \\{{\sin \quad {\phi sin}\quad {\theta cos}\quad \varphi} - {\cos \quad {\phi sin}\quad \theta}} & {{{- \cos}\quad {\phi sin}\quad \theta} - {\sin \quad {\phi sin}\quad \theta}} & {\cos \quad {\theta cos}\quad \phi} & 0 \\0 & 0 & 0 & 1\end{pmatrix}\quad$

[0082] The voxels, or MRI points, corresponding to nodes 310 of theatlas 300 are segmented based on a Maximum A Posteriori (MAP) estimator,step 760. The MAP estimator is a probability computation withstatistical information stored in the atlas. The MAP estimator and itsuse with other types of data are known to one of ordinary skill in theart, as illustrated by way of example in Wells, supra, StatisticalApproach to Segmentation of Single-Channel Cerebral MR Images, RajapakseJ C, et al., IEEE Transactions on Medical Imaging, 1997, Vol. 16, No.2:176-86, and Automated Model-Based Bias Field Correction of MR Images ofthe Brain, Van Leemput, K. et al., IEEE Transactions on Medical Imaging,1999, Vol. 18, No. 10, which are incorporated herein by reference.

[0083] The registration parameters and bias estimation are then updated,step 770, as is known to one of ordinary skill in the art, asillustrated by way of example in Wells, supra, Automated Model-BasedBias Field Correction of MR Images of the Brain, Van Leemput, K. et al.,IEEE Transactions on Medical Imaging, 1999, Vol. 18, No. 10, andMultimodality Image Registration by maximization of Mutual Information,Maes, F. et al., IEEE Transactions on Medical Imaging, 1997, Vol. 16,No. 2, which are incorporated herein by reference. If the target MAP isnot reached, the process repeats, step 780, beginning again withapplication of bias correction to the MR volume, step 740.

[0084] If the target MAP is reached, the registration matrix isprovided, step 790. The registration matrix can include sixteen (16)values, including translation parameters, scaling parameters, and acombination of the sines and cosines of rotation parameters. Theregistration matrix can be used to obtain a specific geometry (e.g.orientation and/or dimensions) of the data acquired during a subsequentscan, as discussed herein.

[0085] The MR volume corrected for bias is also provided, step 800,allowing more accurate computation of the magnetic property values foreach node of the atlas.

[0086] Further information regarding the details of the steps of FIG. 11can be found in Wells, supra, Automated Model-Based Bias FieldCorrection of MR Images of the Brain, Van Leemput, K. et al., IEEETransactions on Medical Imaging, 1999, Vol. 18, No. 10, andMultimodality Image Registration by maximization of Mutual Information,Maes, F. et al., IEEE Transactions on Medical Imaging, 1997, Vol. 16,No. 2, which are incorporated herein by reference.

[0087] According to a further embodiment of the invention, a system 900is provided as shown by way of example in FIG. 12. A scanner 910 isprovided to capture magnetic images. A processor 920 is provided tointerface with the scanner 910 and the atlas 300 in order to conduct themethods according to various embodiments of the present invention.

[0088] The atlas and system 900 of the present invention may be used ina variety of applications. In one embodiment, a method of using theatlas with magnetic property data and optionally with tissue (oranatomical structure) type prior probabilities is provided,automatically align an MR scan, such as a localizer scan, to obtain aspecific geometry of the data acquired during a subsequent scan(auto-slice prescription). Further details of this implementation can befound in U.S. Pat. No. 6,195,409, issued Feb. 27, 2001, to Chang et al.,which is incorporated herein by reference.

[0089] In an additional embodiment, a method of using the atlas withmagnetic property data to determine anatomical structure or detectabnormal tissue (auto-segmentation) is provided. Further details of thisimplementation can be found in Wells, supra, Statistical Approach toSegmentation of Single-Channel Cerebral MR Images, Rajapakse J C, etal., IEEE Transactions on Medical Imaging, 1997, Vol. 16, No.2: 176-86,and Automated Model-Based Bias Field Correction of MR Images of theBrain, Van Leemput, K. et al., IEEE Transactions on Medical Imaging,1999, Vol. 18, No. 10, which are incorporated herein by reference.

[0090] It will further be appreciated that in the methods of the presentinvention, including the applications described herein, distortion ofnewly obtained data may optionally be corrected prior to processing datain conjunction with the atlas. Further details of distortion correctioncan be found in Sources of Distortion in Functional MRI Data, Jezzard,P. et al., Human Brain Mapping, 1999, Vol. 8:80-85, which isincorporated herein by reference.

[0091] The present invention has been described by way of example, andmodifications and variations of the described embodiments will suggestthemselves to skilled artisans in this field without departing from thespirit of the invention. Aspects and characteristics of theabove-described embodiments may be used in combination. The describedembodiments are merely illustrative and should not be consideredrestrictive in any way. The scope of the invention is to be measured bythe appended claims, rather than the preceding description, and allvariations and equivalents that fall within the range of the claims areintended to be embraced therein. The contents of all references,databases, patents and published patent applications cited throughoutthis application are expressly incorporated herein by reference.

What is claimed is:
 1. An atlas comprising a value representative of amagnetic property of a spatial location of a subject as determined bymagnetic resonance.
 2. The atlas of claim 1, wherein said valuecorresponds to a proton density value at a corresponding spatiallocation.
 3. The atlas of claim 1, wherein said value corresponds to aTI value at a corresponding spatial location.
 4. The atlas of claim 1,wherein said value corresponds to a T2 value at a corresponding spatiallocation.
 5. The atlas of claim 1, wherein said value corresponds toproton density, T1 and T2 values at a corresponding spatial location. 6.The atlas of claim 5, wherein said value corresponds to a tissue type.7. The atlas of claim 1, wherein said value corresponds to a tissue typeat a corresponding spatial location.
 8. The atlas of claim 1, whereinsaid value corresponds to a diffusion tensor value at a correspondingspatial location.
 9. The atlas of claim 1, wherein said valuecorresponds to a magnetization transfer value at a corresponding spatiallocation.
 10. The atlas of claim 1, wherein said value corresponds to aT2* value at a corresponding spatial location.
 11. The atlas of claim 1,wherein said value corresponds to an anisotropy value at a correspondingspatial location.
 12. The atlas of claim 1, wherein said valuecorresponds to a diffusivity value at a corresponding spatial location.13. The atlas of claim 1, wherein said value corresponds to acorresponding spatial location in each of a plurality of subjects. 14.An atlas comprising values representative of a statisticalrepresentation of a magnetic property of a plurality of spatiallocations of a plurality of subjects.
 15. The atlas of claim 14, whereinsaid values of a statistical representation include a mean of magneticproperty values at each corresponding spatial location of said pluralityof subjects.
 16. The atlas of claim 14, wherein said values of astatistical representation include a variance of magnetic propertyvalues at each corresponding spatial location of said plurality ofsubjects.
 17. The atlas of claim 14, wherein said values of astatistical representation include a mean and a variance of intensitiesof each of a plurality of magnetic property values at each correspondingspatial location of said plurality of subjects.
 18. The atlas of claim17, wherein said values of a statistical representation further includeprior probabilities of a plurality of tissue types at a plurality ofcorresponding spatial locations of said plurality of subjects.
 19. Theatlas of claim 18, wherein said mean and said variance of each of saidplurality of magnetic property values at each corresponding spatiallocation are determined for each tissue type.
 20. The atlas of claim 14,wherein said values of a statistical representation include a priorprobability of a tissue type at each corresponding spatial location ofsaid plurality of subjects.
 21. The atlas of claim 20, wherein saidvalues of a statistical representation further include a global priorprobability of a tissue type for said plurality of subjects.
 22. Theatlas of claim 20, wherein said values of a statistical representationfurther include prior probabilities of a plurality of tissue types ateach of a plurality of corresponding spatial locations of said pluralityof subjects.
 23. The atlas of claim 14, wherein said values of astatistical representation include global prior probabilities aplurality of tissue types for said plurality of subjects.
 24. The atlasof claim 14, wherein said values of a statistical representation arepopulation-specific.
 25. The atlas of claim 14, wherein said values of astatistical representation are scanner-specific.
 26. The atlas of claim14, wherein said values of a statistical representation are acquisitionsequence-specific.
 27. The atlas of claim 26, wherein said values of astatistical representation contain magnetic resonance sequenceparameters, including at least one from the group consisting of TR, TEand flip angle.
 28. The atlas of claim 14, wherein said atlas containsinformation relative to an RAS coordinate system.
 29. The atlas of claim14, wherein said atlas contains information relative to a Cartesiancoordinate system.
 30. The atlas of claim 14, wherein said atlascontains data relative to image intensity of at least one subject ofsaid plurality of subjects.
 31. An atlas comprising valuesrepresentative of a statistical representation of a plurality ofmagnetic properties of a spatial location of a subject.
 32. The atlas ofclaim 31, wherein said values of a statistical representation include amean of a magnetic property value at said spatial location.
 33. Theatlas of claim 31, wherein said values of a statistical representationinclude a variance of a magnetic property value at said spatiallocation.
 34. The atlas of claim 31, wherein said values of astatistical representation include a mean and a variance of a magneticproperty value at said spatial location.
 35. The atlas of claim 34,wherein said values of a statistical representation further includeprior probabilities of a plurality of tissue types at said spatiallocation.
 36. The atlas of claim 35, wherein, for each of said tissuetypes, said mean and said variance of said magnetic property values atsaid spatial location are determined.
 37. The atlas of claim 31, whereinsaid values of a statistical representation are scanner-specific. 38.The atlas of claim 31, wherein said values of a statisticalrepresentation are acquisition sequence-specific.
 39. The atlas of claim38, wherein said values of a statistical representation contain magneticresonance sequence parameters, including at least one from the groupconsisting of TR, TE and flip angle.
 40. An atlas, comprising: aplurality of nodes corresponding to a plurality of voxels of at leastone subject; at a node of said plurality of nodes, a prior probabilityof a tissue type located at said voxel corresponding to said node; and astatistical value of a magnetic property value of said tissue typelocated at said voxel corresponding to said node.
 41. The atlas of claim40, wherein said statistical value comprises mean and variance.
 42. Theatlas of claim 41: wherein said voxels correspond to a plurality ofsubjects and a plurality of tissue types are located at said node; andwherein said statistical value is comprised of a plurality ofstatistical values calculated for each of said tissue types located atsaid voxel corresponding to said node.
 43. The atlas of claim 40:wherein said voxels correspond to a plurality of subjects and aplurality of tissue types are located at said node; and wherein saidstatistical value is comprised of a plurality of statistical valuescalculated for each of said tissue types located at said voxelcorresponding to said node.
 44. A system for obtaining informationregarding a subject, comprising: a magnetic resonance scanner adapted toobtain a magnetic resonance scan of said subject; an atlas havingmagnetic property values derived from at least one other subject; and aprocessor adapted to receive information from said scanner pertaining tosaid magnetic resonance scan and adapted to read said atlas to enable adetermination of alignment of said magnetic resonance scan to obtain aspecific geometry of a subsequent magnetic resonance scan.
 45. Thesystem of claim 44, wherein a result of said determination of alignmentis automatically communicated to said scanner from said processor toobtain a specific geometry of said subsequent magnetic resonance scan.46. A method for obtaining information about a subject, comprising thesteps of: providing a magnetic resonance scanner; providing an atlashaving magnetic resonance data derived from at least one other subject;processing information received from said scanner pertaining to saidsubject; reading said atlas; and determining alignment of said magneticresonance scan to obtain a specific geometry of a subsequent magneticresonance scan.
 47. The method of claim 46, further comprising the stepsof: communicating alignment data from said processor to said scanner;and automatically aligning said magnetic resonance scan to obtain saidspecific geometry of a subsequent magnetic resonance scan by the use ofsaid alignment data.
 48. The method of claim 46, wherein said step ofproviding an atlas having magnetic property values derived from at leastone other subject involves data derived from a plurality of othersubjects.
 49. The method of claim 46, wherein said atlas of saidproviding step further comprises tissue type prior probability data. 50.A method for obtaining information about a subject, comprising the stepsof: providing magnetic property values corresponding to tissue types andpertaining to said subject; providing an atlas having magnetic propertyvalues derived from at least one other subject; and labeling tissuetypes of a tissue corresponding to said magnetic resonance propertyvalues pertaining to said subject by the use of said atlas having saidmagnetic resonance values derived from at least one other subject. 51.The method of claim 50, wherein said step of providing an atlas havingmagnetic property values derived from at least one other subjectinvolves data derived from a plurality of other subjects.
 52. A methodfor creating an atlas, comprising the steps of: providing a firstmagnetic resonance modality volume pertaining to a subject and dividedinto voxels; recording a magnetic property value in a node of said atlascorresponding to a voxel of said first magnetic resonance modalityvolume.
 53. The method of claim 52, further comprising, before saidrecording step, the step of correcting distortion of said first magneticresonance modality volume; and
 54. The method of claim 52, furthercomprising the steps of: providing a second magnetic resonance modalityvolume pertaining to a second subject and divided into voxels;correcting distortion of said second magnetic resonance modality volume;and updating said magnetic property data in said node of said atlascorresponding to a voxel of said second magnetic resonance modalityvolume.
 55. The method of claim 52, wherein said step of correctinginvolves the correction of distortion caused by at least one of thegroup consisting of chemical shift, magnetic susceptibility,per-acquisition motion, gradients non-linearity, main magnetic fieldnon-homogeneity, eddy currents, and Maxwell effects.
 56. The method ofclaim 52, further comprising the steps of: providing a plurality ofmagnetic resonance modality volumes pertaining to a plurality ofsubjects and each of said plurality of magnetic resonance modalityvolumes divided into voxels; correcting distortion of each of saidplurality of magnetic resonance modality volumes; and updating saidmagnetic property value in said node of said atlas corresponding to avoxel of each of said plurality of magnetic resonance modality volumes;wherein said magnetic property value in said node of said atlas includesstatistical data.
 57. A method for creating an atlas, comprising thesteps of: providing a first magnetic resonance modality volumepertaining to a subject and divided into voxels; providing a labeledvolume indicating tissue types of tissue corresponding to said voxels;correcting distortion of said first magnetic resonance modality volume;extracting magnetic property distribution parameters for each tissuetype identified at each voxel; and recording magnetic property datacorresponding to each tissue type in a node of said atlas correspondingto a voxel of said first magnetic resonance modality volume.
 58. Themethod of claim 57, further comprising the steps of: providing aplurality of magnetic resonance modality volumes pertaining to aplurality of subjects and each of said plurality of magnetic resonancemodality volumes divided into voxels; providing a plurality of labeledvolumes corresponding to said plurality of magnetic resonance modalityvolumes and indicating tissue types of tissue corresponding to saidvoxels; correcting distortion of each of said plurality of magneticresonance modality volumes; and updating said magnetic property data insaid node of said atlas corresponding to a voxel of each of saidplurality of magnetic resonance modality volumes.
 59. A method forcreating an atlas, comprising the steps of: obtaining a voxel intensityfrom an image representative of at least one magnetic modality of avoxel of a subject; derive a magnetic property value from said voxelintensity; and write said magnetic property value to a node of saidatlas corresponding to said voxel.
 60. The method of claim 59, whereinsaid magnetic property value is at least one from the group of T1, T2and PD.
 61. A method for processing an image of a subject, comprisingthe steps of: obtaining an image pertaining to magnetic property valuesof said subject; providing an atlas having magnetic property valuesderived from at least one other subject; aligning said image to saidatlas; segmenting said image into segments; and labeling said segmentsto designate a tissue type of a tissue corresponding to said magneticproperty values pertaining to said subject by the use of said atlas. 62.The method of claim 61, further comprising the steps of: calculating aprior probability of said tissue type at at least one spatial locationof said at least one other subject and said subject; calculating astatistical value of said magnetic property values representative ofsaid at least one spatial location of said at least one other subjectand said subject; and writing said prior probability and saidstatistical value to a node of said atlas corresponding to said spatiallocation.